Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoenc...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 17; no. 1; p. 192
Main Authors: Kang, Miao, Ji, Kefeng, Leng, Xiangguang, Xing, Xiangwei, Zou, Huanxin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 20.01.2017
MDPI
Subjects:
Accuracy
Algorithms
Classification
Feature extraction
feature fusion
Information dissemination
MSTAR
Networks
Neural networks
Redundancy
SAR target recognition
Sensors
stacked autoencoder
Synthetic aperture radar
Target recognition
Wavelet transforms
Beijing China
China
SAR target recognition
feature fusion
MSTAR
stacked autoencoder
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s17010192